Financial data resampling for machine learning based trading : application to cryptocurrency markets

Bibliographic Information

Financial data resampling for machine learning based trading : application to cryptocurrency markets

Tomé Almeida Borges, Rui Neves

(Springer briefs in applied sciences and technology, . Computational intelligence)

Springer, c2021

  • : pbk

Available at  / 2 libraries

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Note

"This Springer imprint is published by the registered company Springer Nature Switzerland AG ... Cham, Switzerland"--T.p. verso

Includes bibliographical references

Description and Table of Contents

Description

This book presents a system that combines the expertise of four algorithms, namely Gradient Tree Boosting, Logistic Regression, Random Forest and Support Vector Classifier to trade with several cryptocurrencies. A new method for resampling financial data is presented as alternative to the classical time sampled data commonly used in financial market trading. The new resampling method uses a closing value threshold to resample the data creating a signal better suited for financial trading, thus achieving higher returns without increased risk. The performance of the algorithm with the new resampling method and the classical time sampled data are compared and the advantages of using the system developed in this work are highlighted.

Table of Contents

Chapter 1 - Introduction Chapter 2 - Related work Chapter 3 - Implementation Chapter 4 - Results Chapter 5 - Conclusions and future work

by "Nielsen BookData"

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